Biomedical subjects

Francis Tuerlinckx

Publications and source records attributed to Francis Tuerlinckx.

7 recordsLinked to original sources

PubMed2006

Statistical inference in generalized linear mixed models: a review.

We present a review of statistical inference in generalized linear mixed models (GLMMs). GLMMs are an extension of generalized linear models and are suitable for the analysis of non-normal data with a clustered structure. A GLMM contains parameters common to all clusters (fixed regression effects and variance components) and cluster-specific parameters. The latter parameters are assumed to be randomly drawn from a population distribution. The parameters of this population distribution (the variance components) have to be estimated together with the fixed effects. We focus on the case in which the cluster-specific parameters are normally distributed. The cluster-specific effects are integrated out of the likelihood so that the fixed effects and variance components can be estimated. Unfortunately, the integral over the cluster-specific effects is intractable for most GLMMs with a normal mixing distribution. Within a classical statistical framework, we distinguish between two broad classes of methods to handle this intractable integral: methods that rely on a numerical approximation to the integral and methods that use an analytical approximation to the integrand. Finally, we present an overview of available methods for testing hypotheses about the parameters of GLMMs.

Analysis of Variance↗

PubMed2005

Mixed model estimation methods for the Rasch model.

Mixed models take the dependency between observations based on the same person into account by introducing one or more random effects. After introducing the mixed model framework, it is explained, by taking the Rasch model as a generic example, how item response models can be conceptualized as generalized linear and nonlinear mixed models. Common estimation methods for generalized linear and nonlinear models are discussed. In a simulation study, the performance of four estimation methods is assessed for the Rasch model under different conditions regarding the number of items and persons, and the degree of interindividual differences. The estimation methods included in the study are: an approximation of the integral over the random effect by means of Gaussian quadrature; direct maximization with a sixth-order Laplace approximation to the integrand; a linearized approximation of the nonlinear model employing PQL2; and finally a Bayesian MCMC method. It is concluded that the estimation methods perform almost equally well, except for a slightly worse recovery of the variance parameter for PQL2 and MCMC.

Data Interpretation, Statistical↗

PubMed2004

The efficient computation of the cumulative distribution and probability density functions in the diffusion model.

An algorithm is described to efficiently compute the cumulative distribution and probability density functions of the diffusion process (Ratcliff, 1978) with trial-to-trial variability in mean drift rate, starting point, and residual reaction time. Some, but not all, of the integrals appearing in the model's equations have closed-form solutions, and thus we can avoid computationally expensive numerical approximations. Depending on the number of quadrature nodes used for the remaining numerical integrations, the final algorithm is at least 10 times faster than a classical algorithm using only numerical integration, and the accuracy is slightly higher. Next, we discuss some special cases with an alternative distribution for the residual reaction time or with fewer than three parameters exhibiting trial-to-trial variability.

Algorithms↗

PubMed2003

A nonlinear mixed model framework for item response theory.

Mixed models take the dependency between observations based on the same cluster into account by introducing 1 or more random effects. Common item response theory (IRT) models introduce latent person variables to model the dependence between responses of the same participant. Assuming a distribution for the latent variables, these IRT models are formally equivalent with nonlinear mixed models. It is shown how a variety of IRT models can be formulated as particular instances of nonlinear mixed models. The unifying framework offers the advantage that relations between different IRT models become explicit and that it is rather straightforward to see how existing IRT models can be adapted and extended. The approach is illustrated with a self-report study on anger.

Anger↗

PubMed2003

Decision-bound theory and the influence of familiarity.

In this article, we derive a nonparametric prediction from decision-bound theory (DBT). The crucial aspect that is tested is whether or not familiarity of a stimulus affects response time in categorization. We show that, for our design, DBT, extended with some reasonable and testable assumptions, predicts no familiarity effect. Our prediction is nonparametric in that, rather than fit a specific instantiation of general DBT, we posit only some general assumptions of this theory and derive the prediction from these assumptions. It is found that familiarity did have a strong impact on response time for at least half of our participants. We suggest that DBT is in itself incomplete and should be extended to account for the full range of available data.

Adolescent↗

PubMed2002

Estimating parameters of the diffusion model: approaches to dealing with contaminant reaction times and parameter variability.

Three methods for fitting the diffusion model (Ratcliff, 1978) to experimental data are examined. Sets of simulated data were generated with known parameter values, and from fits of the model, we found that the maximum likelihood method was better than the chi-square and weighted least squares methods by criteria of bias in the parameters relative to the parameter values used to generate the data and standard deviations in the parameter estimates. The standard deviations in the parameter values can be used as measures of the variability in parameter estimates from fits to experimental data. We introduced contaminant reaction times and variability into the other components of processing besides the decision process and found that the maximum likelihood and chi-square methods failed, sometimes dramatically. But the weighted least squares method was robust to these two factors. We then present results from modifications of the maximum likelihood and chi-square methods, in which these factors are explicitly modeled, and show that the parameter values of the diffusion model are recovered well. We argue that explicit modeling is an important method for addressing contaminants and variability in nondecision processes and that it can be applied in any theoretical approach to modeling reaction time.

Humans↗

PubMed2002

Measuring needs with the thematic apperception test: a psychometric study.

Three apperception theories that explain how people respond to Thematic Apperception Test cards are proposed: a simple apperception theory, an apperception theory with a dynamic component, and an apperception theory with 2 types of responses. Each theory is translated into an item response theory model and is applied to need for achievement (nAch) data. The analysis indicates that the best fitting model is provided by the apperception theory with 2 types of responses, also referred to as the drop-out apperception theory. The 1st type of response predicted by this theory is determined by the nAch level of the person and the achievement-response-eliciting value of the card; this response is diagnostic for the nAch level of the person. The 2nd type of response is not determined by the 2 aforementioned characteristics and is therefore not diagnostic of the person's nAch level. The results are cross-validated for need for power and need for affiliation.

Achievement↗